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On weighted edgesearching
, 2009
"... Summary. In this document we address some complications regarding the weighted edgesearching problem. This variant of edgesearching extends the original problem by considering situations where multiple searchers may be required to clear a single edge or guard a single vertex. We show that previous ..."
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Cited by 5 (3 self)
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Summary. In this document we address some complications regarding the weighted edgesearching problem. This variant of edgesearching extends the original problem by considering situations where multiple searchers may be required to clear a single edge or guard a single vertex. We show
Visualizing Weighted Edges in Graphs
 Proc. of 7th Int'l Conference on Information Visualization (IV '03
"... This paper introduces a new edge length heuristic that finds a graph layout where the edge lengths are proportional to the weights on the graph edges. The heuristic can be used in combination with the spring embedder to produce a compromise between a drawing with an accurate presentation of edge len ..."
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Cited by 2 (1 self)
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This paper introduces a new edge length heuristic that finds a graph layout where the edge lengths are proportional to the weights on the graph edges. The heuristic can be used in combination with the spring embedder to produce a compromise between a drawing with an accurate presentation of edge
FPTAS for Counting Weighted Edge Covers
"... Abstract. An edge cover of a graph is a set of edges in which each vertex has at least one of its incident edges. The problem of counting the number of edge covers is #Pcomplete and was shown to admit a fully polynomialtime approximation scheme (FPTAS) recently [10]. Counting weighted edge covers ..."
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Abstract. An edge cover of a graph is a set of edges in which each vertex has at least one of its incident edges. The problem of counting the number of edge covers is #Pcomplete and was shown to admit a fully polynomialtime approximation scheme (FPTAS) recently [10]. Counting weighted edge covers
A computational approach to edge detection
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1986
"... AbstractThis paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal ..."
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Cited by 4621 (0 self)
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AbstractThis paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
SemiSupervised Learning Using Gaussian Fields and Harmonic Functions
 IN ICML
, 2003
"... An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
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Cited by 741 (15 self)
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An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning
A distributed algorithm for minimumweight spanning trees
, 1983
"... A distributed algorithm is presented that constructs he minimumweight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm and exchange ..."
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Cited by 443 (3 self)
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A distributed algorithm is presented that constructs he minimumweight spanning tree in a connected undirected graph with distinct edge weights. A processor exists at each node of the graph, knowing initially only the weights of the adjacent edges. The processors obey the same algorithm
Inapproximability of maximum weighted edge biclique and its applications
 LNCS
"... Abstract. Given a bipartite graph G = (V1, V2, E) where edges take on both positive and negative weights from set S, the maximum weighted edge biclique problem, or SMWEB for short, asks to find a bipartite subgraph whose sum of edge weights is maximized. This problem has various applications in bio ..."
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Cited by 8 (0 self)
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Abstract. Given a bipartite graph G = (V1, V2, E) where edges take on both positive and negative weights from set S, the maximum weighted edge biclique problem, or SMWEB for short, asks to find a bipartite subgraph whose sum of edge weights is maximized. This problem has various applications
Results 1  10
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633,250